prioritization of sites for plant species restoration in ... · • developing methodological...

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Prioritization of sites for plant species restoration in the Chilean Biodiversity 1

Hotspot: A spatial multi-criteria decision analysis approach 2

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Running Head: Prioritizing sites for plant species restoration 4

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Ignacio C. Fernándeza,b,*, Narkis S. Moralesb,c 6

aLandscape Ecology & Sustainability Laboratory, Arizona State University, LSE Room 704, Tempe, AZ 7

85281, USA. (email: [email protected]). 8

bFundación Ecomabi, Ahumada 312, Oficina 425, Santiago, Chile. 9

cDepartment of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Building 10

E8B Room 206, NSW 2109, Sydney, Australia. (email: [email protected]) 11

*Corresponding author 12

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Author contributions: IF and NM conceived and designed the research; NM performed the niche 14

modeling; IF built the spatial layers and performed the GIS analyses; IF and NM wrote and edited the 15

manuscript. 16

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Abstract 19

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Various initiatives to identify global priority areas for conservation have been developed over the last 20 21

years (e.g. Biodiversity Hotspots). However, translating this information to actionable local scales has 22

proven to be a major task, highlighting the necessity of efforts to bridge the global-scale priority areas with 23

local-based conservation actions. Furthermore, as these global priority areas are increasingly threatened 24

by climate change and by the loss and alteration of their natural habitats, developing additional efforts to 25

identify priority areas for restoration activities is becoming an urgent task. In this study we used a Spatial 26

Multi-Criteria Decision Analysis (SMCDA) approach to help optimize the selection of sites for restoration 27

.CC-BY-NC-ND 4.0 International licensenot certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which wasthis version posted September 13, 2015. . https://doi.org/10.1101/026716doi: bioRxiv preprint

https://doi.org/10.1101/026716

http://creativecommons.org/licenses/by-nc-nd/4.0/

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initiatives of two endemic threatened flora species of the “Chilean Winter Rainfall-Valdivian Forest” 28

Hotspot. Our approach takes advantage of freely GIS software, niche modeling tools, and available 29

geospatial databases, in an effort to provide an affordable methodology to bridge global-scale priority 30

areas with local actionable restoration scales. We used a set of weighting scenarios to evaluate the 31

potential effects of short-term vs long-term planning perspective in prioritization results. The generated 32

SMCDA was helpful for evaluating, identifying and prioritizing best suitable areas for restoration of the 33

assessed species. The method proved to be simple, transparent, cost effective and flexible enough to be 34

easily replicable on different ecosystems. This approach could be useful for prioritizing regional-scale 35

areas for species restoration in Chile, as well as in other countries with restricted budgets for 36

conservation efforts. 37

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Keywords: Conservation planning; Restoration planning; Niche modeling; Maxent; Climate change; 39

Bielschmiedia miersii; Pouteria splendens. 40

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Implications for Practice: 43

• Developing methodological approaches to identify and prioritize areas for restoration activities is a 44

crucial task for restoration planning, especially in regions with limited resources for conservation 45

initiatives. 46

• The increasing availability of free GIS software, niche modeling tools, and geospatial databases offer 47

valuable resources that can be integrated into a spatial multi-criteria decision analysis (SMCDA) to help 48

in the selection of best areas for restoration initiatives. 49

• The SMCDA provide a flexible, transparent, affordable and replicable framework to prioritize regional-50

scale areas for restoration of plant species in Chile, as well as in other countries with restricted budgets 51

for conservation efforts. 52

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Introduction 56

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Humans have extensively and profoundly altered Earth’s landscapes by transforming natural habitats in 58

productive lands and by appropriating a vast extension of available natural resources (Ellis et al. 2010; 59

Ellis & Ramankutty 2008). The replacement of natural habitats by agricultural lands, artificial forests, and 60

urban areas has generated the loss, fragmentation, and degradation of natural habitats leading to an 61

alarming global increase in species extinction rates ( Foley et al. 2005; Brook et al. 2008). While the 62

current biodiversity crisis is far reaching and complex, resources available for planning and developing 63

conservation strategies are still very limited ( Wilson et al. 2009; Watson et al. 2011). Therefore, one of 64

the main challenges for conservation biologist has been the development of methodological approaches 65

that helps in the prioritization of limited resources among different conservation options (Redford et al. 66

2003). 67

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Among these challenges, a frequent task for conservationists has been the selection of areas to be 69

prioritized for conservation actions. At the global scale these initiatives have aimed to identify those 70

ecoregions that are most valuable for conservation around the world (Brooks et al. 2006). Examples of 71

these initiatives are the “Global 200 Ecoregion” (Olson & Dinerstein 2002), the “Crisis Ecoregion” 72

(Hoekstra et al. 2005), and probably the most recognized of all, the world “Biodiversity Hot Spots” ( Myers 73

1990; Myers et al. 2000; Mittermeier et al. 2004). Even though these initiatives have been fruitful in 74

signaling priority areas at a global-scale, their real success has been criticized because they have not 75

provided information regarding how to allocate resources within prioritized ecoregions (Wilson et al. 76

2006). For these initiatives to have real impacts in local conservation actions will require the development 77

of complementary regional and local scales prioritization approaches (Redford et al. 2003). This is a 78

critical issue because the large extent of prioritized areas are in developing countries (Brooks et al. 2006) 79

where budgets for conservation initiatives are often much smaller than is required (Waldron et al. 2013). 80

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At finer scales conservationists have placed a great extent of their efforts to identify and bring under 82

protection the most valuable sites for conservation. These efforts have largely been guided by the use of 83


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“systematic conservation planning ” (Margules & Pressey 2000), which has provided a useful framework 84

to optimize the selection of sites to be targeted for developing conservation strategies (Sarkar et al. 2006; 85

Wilson et al. 2009). In general conservation planning can be defined as “the process of deciding where, 86

when and how to allocate limited conservation resources to minimize the loss of biodiversity, ecosystem 87

services and other valued aspects of the natural world” (Pressey & Bottrill 2009). It concerns the 88

prioritization of sites based in their biodiversity value, and the participatory planning and collaborative 89

implementation of strategies that secure the long-term viability of biological diversity (Kukkala & Moilanen 90

2013). Whereas systematic conservation planning has largely influenced the way institutions and 91

governments prioritize the efforts to protect valuable ecosystems (Sarkar et al. 2006), this systematic 92

approach seems to not have permeated to other fundamental conservation actions, such as restoration 93

planning (but see Noss et al. 2009) 94

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Biodiversity restoration activities are among the most expensive conservation strategies worldwide (Holl 96

et al. 2003). However the development of approaches specifically aimed to prioritize sites for restoration 97

or reintroduction of species has been scarcely addressed (Noss et al. 2009). In contrast to the 98

predominant systematic conservation planning approach that focuses primarily in prioritizing areas that 99

currently contain target species, restoration activities often need to prioritize sites that have reduced 100

populations, or even the complete absence of the species to conserve. Furthermore, because systematic 101

conservation planning has focused primarily on current biodiversity patterns, it has had limited 102

applications for conservation strategies in a rapidly changing climate (Pressey et al. 2007). As a result, 103

species may lose protection as their ranges shift out of current reserve boundaries (Schloss et al. 2011). 104

Therefore the development of complementary efforts that helps decision-makers to identify best areas for 105

restoration activities under a climate change perspective should be taken as a major objective. 106

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The increasing development of freely available spatial software, and the production and release of 108

geospatial data by governments and international organizations have greatly improved our capacity to 109

address spatial conservation planning challenges (Baldwin et al. 2014). Furthermore, the availability of 110

niche modeling softwares and the development and release of future climate projections have also 111


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increased our capability for conservation planning under a climate change scenario (Schwartz 2012). 112

Even though, available planning software, such as Marxan are already capable to handle future 113

environmental variability (e.g. Carvalho et al. 2011; Veloz et al. 2013), they often need large amount of 114

specific data that may not be readily available, or even may not be relevant for regional scale restoration 115

prioritization goals. Moreover, their use may be perceived as complex and challenging by practitioners, 116

which could preclude their application for decision-making (Baldwin et al. 2014). 117

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To overcome these difficulties, the integration of Multi-Criteria Decision Analysis (MCDA) in a Geographic 119

Information System (GIS) could provide an inexpensive, simple, flexible, transparent and replicable 120

approach to integrate available and generated spatial information to prioritize sites for restoration 121

initiatives at a regional scale. From a rudimentary perspective a GIS-based MCDA or Spatial Multi-Criteria 122

Decision Analysis (SMCDA) can be seen as a decision support process that integrates geospatial data 123

and combining rules to obtain information for decision-making (Malczewski 2006). The SMCDA approach 124

provides a framework that takes explicit account of multiple criteria, helps to structure the management 125

problem, provides a model that can serve as a focus for discussion, and offers a transparent process that 126

leads to rational, justifiable, and explainable decisions (Mendoza & Martins 2006). SMCDA has been 127

increasingly used in environmental sciences and forest management in last decades; however it 128

application for selecting sites for restoration have been scarcely explored (Mendoza & Martins 2006; 129

Huang et al. 2011). 130

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The objective of this study was to develop and evaluate a complementary approach to systematic 132

conservation planning that could be widely used to identify and prioritize site for species restoration 133

initiatives at the regional scale. As a representative case study, we focused our analysis on the selection 134

of priority areas for restoration for two threatened endemic tree species Bielschmiedia miersii and 135

Pouteria splendens of the “Chilean Rainfall-Valdivian Forest Biodiversity Hotspot” (Arroyo et al. 2004). 136

These species are dominant trees in their respective ecological communities, are inadequately covered 137

by protected areas, and are increasingly threatened by human driven activities (Hechenleitner et al. 2005; 138

Schulz et al. 2010; Pliscoff &Fuentes-Castillo 2011). 139


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Methodology 140

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Study area 142

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The study area covers the “shrub and sclerophyllous forest ecological region” of Central Chile (Gajardo 144

1994), including the coastal and inner territories that make up the current distribution of B. miersii and P. 145

splendens (Fig. 1). Original landscapes within this ecological region were characterized by a dominance 146

of shrubs species in the coastal ranges, and a mix of forest and shrubs in more inland areas (Gajardo 147

1994). This region has been severely transformed by the fragmentation of the original landscape, and the 148

remaining habitats are increasingly threatened by human activities (Pliscoff & Fuentes-Castillo 2011). 149

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Climate within the study area can be broadly characterized as Mediterranean, with marked colder 151

temperatures and rainy periods during winter months, and warmer and dry period during summer 152

(Luebert & Pliscoff 2006). However, local climate characteristics differ considerably over the geographical 153

range of the study area. Historical data for the northern city of Illapel (31°37’50’’ S; 71°09’55’’ W) registers 154

an annual total precipitation of ∼240 mL, while data for the southern city of Rancagua (31°54’43’’ S; 155

71°30’39’’ W) reports a yearly total precipitation of ∼500 mL. Annual average temperatures are similar 156

over the study range (around 12 - 13°C), but thermic oscillation during warmer and colder seasons differs 157

between the coastal and inland territories (Dirección Meteorológica de Chile 2001). For example, the 158

inner city of Santiago (33°27’50’’ S; 70°38’26’’ W) has an average maximum temperature of 29.7°C during 159

summer months and a minimum average temperature of 3.9° C during winter, whereas for the same 160

period the maximum and minimum temperatures for the coastal city of Valparaiso (33°02’42’’ S; 161

71°37’14’’ W) are 20.8°C and 9.2°C respectively (Cruz & Calderón 2008). 162

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Spatial Multi Criteria Decision Analysis (SMCDA) 168

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To identify the best suitable areas for restoration with B. miersii and P. splendens we performed a GIS-170

based multi criteria decision analysis (Malczewski 2006) consisting in the integration of four spatial raster 171

layers by using a weighted scheme (Eq. 1). 172

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PRS� � W�� x PHS�+ W�� x FHS�+ W�� x LUT� + W�� x PSC�

(Eq. 1) 174

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We designed the equation and layers to produce a Priority for Restoration Score (PRS) ranging from 0 to 176

10 for each species (i), with higher values indicating better areas for restoration. Weights (Wx) are specific 177

for each variable and their sum must be equal to 1. Spatial layers in Equation 1 are: Present habitat 178

suitability (PHS), used as an indicator of the relative quality of each pixel’s current climatic conditions for 179

the occurrence of the assessed species. Future habitat suitability (FHS), used as an indicator of the 180

relative quality of each pixel’s future climatic conditions for the occurrence of the assessed species. Land-181

use type (LUT), which represents the current availability of lands with potential conditions to start a 182

restoration project with the studied species. Priority sites for conservation (PSC), which are areas 183

prioritized by the Chilean government for future conservation initiatives, and used as an indicator of the 184

suitability of each pixel to hold restoration activities in the long-term. The specific methods to generate 185

these four layers are explained below. 186

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Present Habitat Suitability (PHS) 188

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We used Maxent software version 3.3.3k (Phillips et al. 2006; Phillips & Dudík 2008; 190

http://www.cs.princeton.edu/~schapire/maxent) to model present and future potential habitat suitability of 191

B. miesrsii and P. splendens. We input georeferenced data of recorded individuals of both species as 192

presence points and climatic geospatial data gathered from the WorldClim database 193

(http://www.worldclim.org). Worldclim database consists in 19 bioclimatic layers generated from the 194


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interpolation of climatic data compiled around the world with a resolution of ∼1 Km2 (Hijmans et al. 2005). 195

Species-presence data was obtained from herbarium specimens, literature records, previous species 196

surveys, and data collected in several field campaigns during the year 2011. We aggregated species 197

presence points to match climatic data resolution avoiding pseudo replication. A total of 75 presence 198

points for B. miersii and 22 for P. splendens were included in the modeling procedure. 199

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To ensure the quality of the final habitat suitability models and to reduce potential over-parameterization ( 201

Williams et al. 2003; Merow et al. 2013) we performed a Pearson correlation analysis of the 19 bioclimatic 202

variables using for that the software ENMTools version 1.4.4 (Warren et al. 2010). As suggested by 203

previous studies (Kumar & Stohlgren 2009), all variables with correlations larger than 0.8 were evaluated 204

to retain only those more relevant for the species ecology. After the correlation analysis, only 8 out the 19 205

bioclimatic variables were included in the modeling of B. miersii and P. splendens suitable habitats (See 206

supplementary data). 207

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To select the model parameters we generated and compared several different models by utilizing the 209

corrected Akaike information criterion (AICc) available in the software ENMTOOLS version 1.4.4 (Warren 210

et al. 2010). We took this approach, instead of using Maxent default parameters, as recent studies have 211

shown that could provide better results than Maxent standard configuration (e.g. Merow et al. 2013; Syfert 212

et al. 2013), especially when modeling with a small number of samples (< 20 - 25) (Shcheglovitova & 213

Anderson 2013). The results suggested that the best combination of parameters were HQPT with a 214

regularization multiplier of 2 for B. miersii and LQ with a regularization multiplier of 1 for P. splendens 215

(See supplementary data). 216

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To generate the final model for B. miersii we used a random sample of 75% of the presence points, while 218

the 25% remaining points were used to validate the model. The model efficiency was analyzed by the 219

reported AUC (area under the curve). The generated distribution model for B. miersii showed an AUC of 220

0.974, which is classified as an excellent predicting performance (Elith 2000). 221

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In the case of P. splendens we followed the “Jackknife” model testing procedure proposed by Pearson et 223

al. (2007), which is especially helpful for bioclimatic modeling with small presence point data. The 224

procedure consists in removing one presence point from the original dataset (n=22) to subsequently run 225

the model with the remainder (n – 1, 21 points) presence points. This process was repeated for all 226

presence points generating 22 different models. We then analyzed P. splendens modeling performance 227

by using the software “pValuecompute v.1.0” (Pearson et al. 2007). Performance test for the modeled 228

distribution showed good predicting capability, with a success of 86% (p < 0.001). 229

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Finally we built the PHS raster layers by standardizing the data produced by the Maxent modeling phase 231

into values ranging from 0 to 10. We did this by dividing the probability values of each pixel by the 232

maximum probability value obtained in the entire grid, and then multiplying the resulting value by 10. 233

Generated layer was then resampled to 100 m/pixel through the bilinear interpolation method. 234

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Future Habitat Suitability (FHS) 236

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To generate the future habitat suitability layer under a hypothetical climate change scenario, we re-238

projected the models generated in the PHS section by using projected climatic data for the period 2041-239

2060. We used the climatic projection model known as HadGEM2-ES (Jones et al. 2011) because of its 240

good overall performance in predicting the seasonal variability of precipitation and temperature in South 241

America (Cavalcanti & Shimizu 2012). We chose the RCP 2.6 scenario as a conservative representation 242

of concentrations pathways (RCP) of greenhouse gases (Moss et al. 2010). The RCP 2.6 scenario 243

assumes that the global greenhouse emissions will have their maximum concentration between the years 244

(2010 - 2020), declining after this period. The new climatic layers were downscaled and calibrated using 245

WorldClim 1.4 as the baseline “present” climate (Hijmans et al. 2005). The same bioclimatic variables 246

selected to build the distribution model under the current climatic conditions for each species were used 247

to perform the modeling of future potential habitat suitability. The final FSH raster layer was standardized 248

in values ranging between 0 and 10 and resampled to 100 m/pixel by using the same process used to 249

generate the PSH layer. 250


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Land-use type (LUT) 251

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We used the Chilean national forest inventory spatial dataset (available at http://sit.conaf.cl/) to categorize 253

land-use within the study area. This polygon-based data set was recently updated and is one of the main 254

tools used by the government to develop policies regarding to forest conservation and management 255

(CONAF-Corporación Nacional Forestal 2011). We reclassified the more than 50 land-use type 256

categories present in the inventory in three main classes (i.e. urban, productive, natural) based on the 257

urban-rural-natural spatial pattern present in central Chile (Schulz et al. 2010). Urban lands encompassed 258

all areas classified as urban or industrial by the forest inventory, and were considered not suitable for 259

restoration and therefore given a value of 0. We based this decision on the large difficulties involved in 260

recovering urban and industrial land into areas suitable for restoration initiatives (Pavao-Zuckerman 261

2008). Productive lands included all areas used for agriculture and silvicultural activities and were given a 262

value of 5. This intermediate score represented areas that could be suitable for restoration, but where 263

restoration projects will face several difficulties due to private land-use conflicts and soil restoration 264

challenges (Rey Benayas & Bullock 2012). Natural lands grouped all areas covered by natural vegetation 265

communities, and were given a value of 10. The resulting reclassified vector layer was then converted to 266

a raster layer with a 100 m/pixel resolution. 267

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Priority Sites for Conservation (PSC) 269

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Priority Sites for Conservation are specific areas identified by the Chilean government as the most 271

important zones to develop private or public conservation efforts. There are a total of 68 Priority Sites for 272

Conservation in Chile, and they represent a proactive mechanism specified in the Chilean Biodiversity 273

National Strategy to promote initiatives focused on conservation and protection of biodiversity in the long-274

term (Conama 2003; Pliscoff & Fuentes-Castillo 2011)We used the latest version of the PSC vector layer 275

available from the Chilean Environmental Ministry map service (http://ide.mma.gob.cl). From this layer we 276

selected only the PSCs that were within our study area. We assigned a value of 10 to all the areas within 277

defined priority sites for conservation, and a decreasing values of one unit (i.e. 9,8,7…0) for every one 278


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kilometer of distance to the priority area. Therefore, all the areas that are farther than 9 km from a PSC 279

have a value of 0. Our scoring scheme for the PSC layer (i.e. distance based) follows the 280

recommendation of the Convention on Biological Diversity (Article 8) to develop complementary 281

sustainable development efforts near protected and other areas of conservation importance (United 282

Nations 1992). The resulting vector layer was then converted to a raster layer with a 100 m/pixel 283

resolution. 284

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SMCDA Weighting and Analysis 286

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We developed five weighting scenarios to evaluate the effect of different approaches on habitat 288

prioritization results. The weighting scenarios covered a gradient from an extreme short-term to an 289

extreme long-term approach (Table 1). The extreme short-term scenario (EST) allocates all the weight to 290

the layers related with the current feasibility to start restoration projects (i.e. PHS, LUT), disregarding the 291

contribution of the layers implicated with the long-term viability of restoration projects. At the other hand, 292

the extreme long-term scenario (ELT) allocates all the weight to the layers associated with long-term 293

viability of restoration activities (i.e. FHS, PSC), but disregards factors that may be important for the 294

current implementation of the project. Between these extreme scenarios we built three intermediate 295

weighting scenarios, including a short-term (ST), a non-weighted (NW), and a long-term (LT) scenario 296

(Table 1). 297

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All GIS processing was performed using the free GIS platform Quantum GIS 2.6 Brighton (www.qgis.org). 299

Output layers generated from the SMCDA were “masked” to fit only the areas potentially suitable for the 300

assessed species. We did this by creating a masking layer composed by the aggregated area of present 301

and future niche modeling distribution. We used the “minimum presence threshold” value to set the 302

distribution boundary for each species. All final raster layers were translated to prioritization maps for 303

qualitative evaluation, and areas corresponding to the highest suitability scores (i.e. 9 and 10) where 304

computed for quantitative analysis. 305

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Results 307

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Input layer results 309

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The six raster layers we generated to be used as inputs for the SMCDA are shown in Fig. 2. Even though 311

the main objective of this work was to develop a simple method for prioritizing site for restoration under 312

different scenarios, we consider it relevant to briefly describe the resulting layers that were used as the 313

input variables for our method. This is not only important for placing results in context, but also to provide 314

information that helps to evaluate the SMCDA results. 315

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Modeled distribution under current climate of B. miersii shows a concentration of highest values in the 317

mountainous range covering the northwestern part of the species current distribution. This is coincident 318

with the area that concentrates the large extent of recorded populations. However, the model also 319

assigned intermediate and lower values to several areas were currently populations occur, as those in the 320

central and southern part of current distribution (Fig. 2a). The projected distribution with climate change 321

does not show major differences with the projection under current climatic conditions, except for a slight 322

increase in the range of highest values toward the south and east (Fig. 2b). 323

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In the case of P. splendens, the modeled distribution under current climate is almost entirely restricted to 325

the coastal plains, ravines, and valleys that are directly influenced by the ocean climate. Highest values 326

for P. splendens tend to be localized in three main areas located in the northern, central and southern 327

range of predicted distribution. Whereas the southern and central areas coincide with historic presence 328

records for the species, there are no historic records for the northern area (Fig. 2c). In contrast with B. 329

miersii, the modeled distribution for P. splendens under climate change shows important changes when 330

compared to the current climate conditions, experiencing a general increase of highest values towards 331

the east, which are mostly concentrated in the central part of the species current distribution range (Fig. 332

2d). 333


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Land-use types presenting the highest values tend to be concentrated in the northern part of the study 334

area. These high value areas present a gradual reduction of prevalence towards southern zones where 335

seems to be confined mostly to the upper part of valleys (Fig. 2e). Areas presenting land-use types with 336

intermediate values are mostly represented by valleys and flat areas which are majorly concentrated in 337

the southern part of the study area. Areas presenting the lowest values are concentrated in the eastern 338

part of the area of study. This is coincident with the presence of the Andes Mountain Range, which due to 339

their altitude, topography and severe climatic conditions generate different land covers (e.g. volcanic 340

debris, glaciers, bare rock) that are not viable for restoration with the assessed species (Fig. 2e). 341

342

Areas defined as priority sites for conservation (PSC) by the Chilean government are not evenly 343

distributed in the study area, which is clearly seen as the concentration of highest values in the central 344

and southern parts of the study area, and a complete absence of these sites in northern area (Fig. 2f). 345

Furthermore, PSC are mostly concentrated in the coastal mountainous range (between the Andes 346

mountain and the coast), whereas the coastal plains and zones adjacent to the ocean present only small 347

and highly isolated PSC (Fig. 2f). 348

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SMCDA results 350

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The generated Priority for Restoration Scores (PRS) maps for the five weighting schemes for B. miersii 352

and P. splendens are shown in Figure 3. These maps show the distribution of PRS, which represent the 353

ranked suitability of different areas for developing restoration activities under the five different weighting 354

scenarios. 355

356

There are important differences in the spatial patterns of PRS between the different weighting scenarios. 357

In general, both for B miersii and P. splendens, there is a decreasing average pixel PRS and increasing 358

spatial clustering when moving from the EST to the ELT scenario (Table 2). The fragmented patterns of 359

highest suitable areas shown in short-term scenarios are associated with the projected spatial distribution 360


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of current population (Figure 2a,c), whereas the clustered pattern of long-term scenarios are highly 361

associated with the presence of priority sites for conservation (Figure 2,f). 362

363

While the PRS under the five scenarios show general spatial patterns common to both species, the 364

amount of areas prioritized in the two highest suitability scores does not follow the same patterns (Fig 4). 365

In the case of B. miersii the scenario prioritizing the larger amount of areas with the two highest suitability 366

scores (i.e. 9 and 10) is the EST, whereas for P. splendens is the ELT, which is the opposite scenario. At 367

the other hand, the scenario prioritizing the smaller amount of areas for B. miersii is the LT, whereas for 368

P. splendens is the ST. 369

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Discussion 372

373

Results from our work highlight the usefulness of using a SMCDA approach to identify, evaluate and 374

prioritize sites for restoration at a regional scale. By using this approach we were able to identify and 375

quantify the best suitable areas for restoration initiatives of two threatened endemic species of the 376

Chilean Biodiversity Hotspot. The SMCDA provided a simple and transparent methodological framework 377

to integrate available spatial information, generating insightful knowledge readily usable by local decision-378

makers. Although we could have use additional spatial information as input layers for our approach, we 379

attempted to focus our analysis to spatial layers that can be gathered or easily generated elsewhere. In 380

view of that the aim of this work was not to present the proposed method as a definitive approach –and 381

neither our maps as definitive results, but rather to put in perspective the usefulness of the SMCDA as a 382

tool for spatial prioritization of sites for biodiversity restoration. 383

384

One of the key steps in the developing of a multi-criteria spatial analysis for conservation planning should 385

be the selection of the input layers and the specific weighing applied to each of them (Phua & Minowa 386

2005; Huang et al. 2011). These decisions do not only have to be focused on combining relevant 387

available information, but also must produce legitimate results for decision makers (Munda 2005). 388


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Therefore, the relevancy, accuracy and reliability of the information contained in the input layers are key 389

factors for the quality and credibility of our results 390

391

Input layers 392

393

While we could theoretically include a large number of input variables in our SMCDA, the limited 394

availability of spatial information with adequate resolution importantly reduced the number of potential 395

input layers we could use. Furthermore, because the scope of our work was to develop a methodological 396

framework that can be used elsewhere, selection of input layers not only had to be based in the available 397

spatial information in our study area, but also in other regions with similar conservation challenges. 398

399

In this regard, we are aware that several regions may not have updated and accurate available land-use 400

layers as we had, or if they have, the accessibility to the information could be restricted to governmental 401

agencies. However this limitation could be solved by using freely available land-classification software 402

and satellite images, which is a methodology that can accurately classify land cover in the three 403

categories we used in our approach (e.g. Nolè et al. 2015). Even though the of land-use in only three 404

categories (i.e. urban, rural, natural) can be considered too broad for local-scale conservation planning, it 405

could provide useful information about regional-scale availability of natural lands that can be currently 406

targeted for restoration activities with focal species. 407

408

The inclusion in the SMCDA of official recognized “priority sites for conservation (PSC)” is one of the 409

major novelties of our approach. In contrast with traditional protected areas, PSC are mostly areas not 410

currently protected, but officially recognized as primary importance to be protected in the short to mid-411

term (Conama 2003; Tognelli et al. 2008). PSC inclusion provide an objective indicator on the specific 412

areas that governments would support for future conservation initiatives, which is a fundamental 413

information for planning restoration initiatives under future climate change scenarios. Because the 414

designation of PSC is based in the Convention on Biological Diversity (United Nations 1992; article 8 415

letters a and b), PSC’s represent information that should be available in several of the 194 countries 416


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16

signatories of this convention, and therefore supports its inclusion in the SMCDA as a commonly 417

available spatial information. 418

419

The use of niche modeling software is a key part of our approach because provides fundamental 420

ecological information regarding the areas that have current and future environmental conditions to 421

potentially support restoration projects with the focal species. Additionally, results from the niche 422

modeling phase, such as the most relevant environmental variables related to each species, may also 423

contribute to understand what are key environmental conditions related with the occurrence of species, 424

which can provide useful ecological information for the development of local scale restoration 425

management strategies (Schwartz 2012). However, while niche modelling software are powerful tools, 426

the accuracy of predicted distribution results will depend on the quality of presence data, environmental 427

layers, and parameter settings ( Loiselle et al. 2008; Costa et al. 2010; Soria-Auza et al. 2010) In this 428

regard, researchers and practitioners need to be particularly cautious of potential data flaws if they are to 429

use niche modelling to generate the species distribution layers to be used in the SMCDA. 430

431

Scenarios weighting scheme 432

433

The main characteristic of our weighting scheme was to explicitly relate the weighting scenarios with the 434

underlying characteristics of the used spatial layers in terms of their relevance for short-term and long-435

term decision-making. Each of the five built scenarios has an implicit narrative behind that helps to 436

explain decision-makers the specific weight assigned to each variable. Our assumption was that if 437

restoration strategies are based in short-term planning, current environmental conditions and available 438

natural lands will prevail in decisions. However, if strategies are developed to embrace long-term 439

perspectives, future environmental conditions and projected protected lands need to be increasingly 440

taken into account. 441

442

Even though this weighting scheme was designed to be easily communicated to decision-makers, we did 443

not include a participatory phase to integrate potential decision-makers diversity of opinions. This 444


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17

participatory phase is often considered a fundamental step to legitimate the results in building 445

conservation policies (Munda 2005; Ananda & Herath 2008). However, as we have stated before, our 446

work was not focused on generating a definitive outcome, but rather on evaluating the potential 447

application of the SMCDA approach as a tool for restoration prioritization initiatives. Although our 448

weighting scheme has not passed through a legitimatization phase by decision-makers, we still consider 449

that our results could be helpful because the gradient weighting scheme provide decision-makers with a 450

range of outcomes that can be used as exploratory boundaries to evaluate potential results. Indeed 451

participatory phases often derive in a range of weights, and thus in more than one set of results that are 452

finally used to make a decision (Chen et al. 2010). The use of a gradient weighting scheme when using a 453

small number of variables could also be used to evaluate the sensibility of the SMCDA to changes in 454

weighting values, and therefore to understand what specific values play larger roles in generated results. 455

456

SMCDA results 457

458

Outcomes generated through the SMCDA reveals the specific effects of weighting scenarios and the 459

spatial interaction of spatial layers in the distribution and extent of areas identified with high priorities for 460

developing restorations initiatives with the two species used in our study. These results not only provide 461

useful information for local decision-makers, but also help to understand the role of the different input 462

layers in final outcomes. 463

464

Results of the SMCDA for B. miersii show a significant larger amount of prioritized area for restoration 465

when compared with the suitable areas for P. splendens independently of the assessed scenario, which 466

was an expected result based in the much larger distribution of the former species compared to the later. 467

However, an interesting result from our work was that the total suitability areas for restoration of each 468

species changed in opposite direction when moving from the short-term to the long-term weighting 469

scenarios. Whereas weighting emphasizing long-term scenarios tended to reduce the availability of 470

suitable habitats for B. miersii restoration, these same scenarios increase the extent of suitable areas for 471

P. splendens restoration projects. As two of out of the four layers (i.e. PSC, LUT) were shared for both 472


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18

species in the SMCDA, the divergences of these results are mainly related with the change on the 473

species distribution due to the climate change scenario. In fact, while B. miersii is predicted to see 474

reductions in its potential distribution under the future climate, P. splendens is expected to have an 475

opposite response, presenting a large increase in projected distribution. This species specific response to 476

climate change highlights the importance to take into account the future climate variability for planning 477

plant species restoration initiatives (Gelviz-Gelvez et al. 2015). Because these two species are dominant 478

trees in their respective ecological community, they can be used as proxy for selecting priority sites for 479

restoration efforts focused in the entire vegetation community. 480

481

482

Conclusion 483

484

In this work we demonstrate the usefulness of integrating available spatial layers and niche modeling into 485

a SMCDA approach to develop an affordable, flexible, transparent, and replicable method to prioritize 486

areas for restoration initiatives of plant species taking into account the future climatic variability. It is 487

affordable because it can be performed by using free software (i.e. Maxent, QGIS) and freely available 488

spatial information. It is flexible because input layers, layer scoring, and weighting schemes can be 489

modified to fit specific decision-making contexts. It is transparent because each of the steps is clearly 490

identified and justified. And it is replicable because it uses information that can be gathered or generated 491

elsewhere. Finally, our approach does not aim to be used in replacement of other local-scale software-492

based planning approach, such as Zonation and Marxan, but rather as a complementary method that 493

bridge the global-scale priority areas, with local-scale restoration planning efforts. This SMCDA approach 494

could be used as a management tool to prioritize regional-scale areas for restoration of plant species in 495

Chile, as well as in other countries with restricted budgets for conservation efforts. 496

497

498

499

500


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Acknowledgments 501

502

We would like to thank Gloria Montenegro, Eduardo Arellano and Luis Olivares from the Faculty of 503

Agronomy and Forestry Engineering of the Pontifical Catholic University of Chile for their support during 504

this research. This project was partially funded by the Chilean Native Forest Research Grant, Project 505

CONAF 025/2010: “Distribución, hábitat potencial y diversidad genética de poblaciones de Belloto del 506

Norte (Beilschmiedia miersii) y Lúcumo chileno (Pouteria splendens)”, 507

508

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Tables 649

650

651

Table 1. Scenario weighting scheme used for both species. Scenarios are: EST; extreme short-term, ST; 652

short-term, NW; non-weighted, LT; long-term, ELT; extreme long-term. 653

Layers Scenario Weighting

EST ST NW LT ELT

Present Habitat Suitability 0.50 0.40 0.25 0.20 0.00

Future Habitat Suitability 0.00 0.20 0.25 0.40 0.50

Land-Use Type 0.50 0.30 0.25 0.10 0.00

Priority Sites for Conservation 0.00 0.10 0.25 0.30 0.50

654

655

Table 2. Main statistical summary of PRS for the five evaluated scenarios for each of the assessed 656

species. Mean, coefficient of variation, and Moran’s I coefficient of autocorrelation are shown. Scenarios 657

are: EST; extreme short-term, ST; short-term, NW; non-weighted, LT; long-term, ELT; extreme long-term. 658

B. miersii P. splendens

EST ST NW LT ELT EST ST NW LT ELT

Mean 6.19 5.35 5.15 4.50 4.09 4.73 4.42 4.31 4.07 3.88

CV 0.29 0.31 0.33 0.41 0.58 0.37 0.35 0.35 0.41 0.51

Moran’s I 0.93 0.96 0.97 0.98 0.99 0.88 0.94 0.95 0.98 0.98

659

660

661

662

663

664

665

666

667

668


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Figure Captions 669

670

671

Figure 1. Study Area. The shaded area corresponds to the “shrub and sclerophyllous forest” ecological 672

region, which was used as the boundaries for the niche modeling process. Rectangular area includes the 673

range of distribution of both species, and corresponds to the extent used for creating the maps showed in 674

following sections. Cities mentioned in the text are shown. 675

676

Figure 2. Visualization of the spatial layers built and used as input for the SMCDA. Letters a) and b) 677

represent the modeled present (PHS) and future (FHS) habitat suitability for B. miersii. Letters c) and d) 678

represent the present and future (PHS and FHS) modeled habitat suitability for P. splendens. In a) and c) 679

black dots correspond to the record presence points of these species that were used for the niche 680

modeling process. Letter e) corresponds to the land-use type (LUT) layer, and letter f) to the priority sites 681

for conservation (PSC) layer. 682

683

Figure 3. Suitability of areas for restoration with B. miersii (top) and P. splendens (bottom) under the five 684

assessed scenarios generated through the SMCDA. Values represent Priority for Restoration Score 685

(PRS). Scenarios are: EST; extreme short-term, ST; short-term, NW; non-weighted, LT; long-term, ELT; 686

extreme long-term. 687

688

Figure 4. Quantification of total area categorized in the two highest PRS scores (9 and 10) under the five 689

scenarios for each of the two assessed species, a) B. miersii, b) P. splendens. 690

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